Steve Dale led our seminar which was an ‘open session’ aimed to encourage discussion and challenge. He examined these questions : What is KM ? Does KM get confused with IM. What is the difference ? Does the size of an organization determine the success or failure of a KM strategy ? In the 1990’s KM was recognised as a discipline. Is it still fit for purpose in the 21st century ? What needs to change ? Can AI help KM practitioners ? How ? What prior knowledge, competencies and skills make a good KMer ? What is your experience of KM – good or bad ?
Steve is the founder of Collabor8now which focuses on developing collaborative environments (e.g. Communities of Practice) and the integration of enabling technologies and processes, including KM, IM, Big Data and AI.
He is a certified knowledge manager with the Knowledge Management Institute (KMI) and the author of several published research papers on collaborative behaviours and information technology. Over a 30 year plus career he has led over 40 major change programmes using knowledge and information management techniques and motivational learning strategies.
He occasionally blogs at stephendale.com and collabor8now.com and tweets at @stephendale
What is KM ? How do you document, store, communicate and apply knowledge in an organization in order to improve the processes of that organization. In essence ‘ how do you get the right knowledge to the right person at the right time’. Academics refer to Explicit Knowledge and Implicit / Tacit Knowledge.
Explicit Knowledge is codified and stored and ready to be shared with others. Tutorial videos, databases, memos, books and blogs. Implicit (Tacit) Knowledge is the knowledge inside our heads. This is experience, intuition and natural talent gained over the years. The big consulting firms offered KM as a service from the mid 1990’s. KM Standard – ISO 30401 : 2018. ‘The standard sets requirements and provides guidelines for establishing, implementing, maintaining, reviewing, and improving an effective management system for knowledge management in organizations. All the requirements of this document are applicable to any organization, regardless of its type or size, or the products and services it provides’. There has been a lot of theorizing about the interaction between explicit knowledge and tacit knowledge. Nonaka and Takeuchi modelled a ‘Knowledge Spiral’. Knowledge follows a cycle in which inplicit (tacit) knowledge is ‘extracted’ to become explicit knowledge and explicit knowledge is ‘re-internalised’ into implicit knowledge. The model is called SECI (Socialisation, Externalisation, Combination, Internalisation).
There has been a lot of debate and many diagrams.
Does any of this theorizing tell us what it means to be a good Knowledge Manager or how to apply KM to ‘improve value’ to an organization ?
If we compare manufacturing with offices – regarding ways of working – over the past 70 years then manufacturing has seen huge changes but office work, despite automation, has not developed as radically.
Steve identified four Knowledge Eras of increasing complexity. 1995-2022.
It began with Information Management in the mid-1990’s leveraging explicit knowledge with the key notion of collecting as exemplified by knowledge repositories, best practice, search, taxonomy and residing in artefacts and libraries. This led on to leveraging tacit knowledge in the form of connecting via experience management as exemplified by communities of practice, expert locators AAR’s (After Action Reviews). In turn, leveraging collective knowledge via collaboration led on to Sharepoint, Slack, Google docs, Crowd sourcing. This was / is networked knowledge. Now, by leveraging the creation of knowledge we have ‘sensemaking’ with agile, design thinking, complexity – a ‘new era’. This augmented knowledge is about creating which, in turn, requires a high level of analytical skill. Moreover, the process itself is melded from ‘diverse and unexpected data sources’. Innovation is paramount and it may be disruptive.
Mass connectivity, AI machine learning and data ubiquity are huge stimuli.
Of course there are real concerns in this era. However, it was time to move on to the Table Discussions.
An essential part of a NetIKX seminar are the ‘round table’ discussions by participants after the speaker has finished his talk.
This blog will now focus on the roundtable discussions on three tables and it will bring out the ideas, themes and suggestions put forward by participants and committed to paper. It will compare these with what came out of research on Chat GPT by our Speaker Steve Dale who issued one page of his results to each table as appropriate.
N.B. On each table one collator entered their chosen words or phrases using a black marker pen on a large white sheet of paper (flipchart). All words and / or phrases marked down have been listed.
N.B.B. Chat GPT sheets have been summarised below, in essence only lead headings from each paragraph have been listed. However, the concluding paragraphs are replicated in full.
Table 1 : Theme – Enumerate the processes / technologies / skills that could or should be part of KM.
The participants put foward the following :-
Learning from experience ; business analysis ; action plan ; good communication skills ; recognising human element ; listening ;
change management ; governance model ; technology ; agility ; metrics ;
organisation culture ; cost benefit ; curation ; compliance ; horizon scanning ;
story telling ; risk management ; ethical use of AI.
Chat GPT : Can AI help KM practitioners ?
Knowledge discovery ; natural language processing ; chatbots and virtual assistants ; personalization ; predictive analytics.
Overall, AI can help KM practitioners streamline their processes, improve the quality of KM practices, and ultimately drive better business outcomes.
Table 2 : What prior knowledge, competencies and skills make a good Knowledge Manager ?
The participants put forward the following :-
Communicating, recording an idea so that it is simple to understand and the human element – influence, engage, foster a culture of collaboration.
Understanding our changing world ; communities of practice ; managing versus controlling ; eliminating silos ; cross-discipline department ; people sharing what their roles are ; technologies – exponential level of changes ; understanding the fall out and the cost – harder to manage ; missing body language.
Chat GPT : Knowledge of the organization ; knowledge of KM concepts and practices ; communication and interpersonal skills ; project management skills ; analytical and problem solving skills ; continuous learning.
In summary, a good Knowledge Manager should have a mix of technical, analytical, communication, and interpersonal skills, combined with a deep understanding of the organization and KM techniques.
Table 3 : Does the size of an organisation determine the success or failure of a KM strategy ?
The participants put forward the following :-
KM can work or not in any sized organisation ; success is more dependent on culture, need, and governance ; so many extenauting factors – internal politics, embarassment, redundancies, sufficient resourcing, management integrity, position with an organisation.
Chat GPT : The size of an organization does not necessarily determine the success or failure of a KM strategy. Success depends on .. culture, leadership, resources, and commitment to KM strategy.
Small organizations can be more agile and adaptable, fewer bureaucratic hurdles to overcome.
Large organizations have more resources and expertise, they may have more complex KM challenges.
Ultimately, the success or failure of a KM strategydepends on how well it is aligned with the organization’s goals, culture and processes, and how effectively it is implemented and sustained over time. A well-designed and executed KM strategy can benefit organizations of any size, while a poorly designed or implemented strategy can fail regardless of size.
The conclusion ? While not trying to compare ‘man’ with ‘machine’ directly there is good evidence that Chat GPT is very impressive and that AI has arrived with a ‘big bang’ and of course, here we are talking about three ‘teams’ of men and women working together on three separate tables and up against one machine.
N.B.B. ‘Deus ex machina’. Modern Latin quote (originally in Greek). It means ‘god from the machinery’. In Greek theatre , actors representing gods were suspended above the stage, the denouement of the play being brought about by their intervention.
Am I being unfair to the human factor ?